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Abstract

The Industry 4.0 Standard (I4S) employs technologies for automation and data exchange through cloud computing, Big Data (BD), Internetof Things (IoT), forms of wireless Internet, 5G technologies, cryptography, the use of semantic database (DB) design, Augmented Reality (AR) and Content-Based Image Retrieval (CBIR). Its healthcare extension is the so-called Health 4.0.This study informsabout Health 4.0 and its potential to extend, virtualize and enable new healthcare-related processes (e.g., home care, finitude medicine, and personalized/remotely triggered pharmaceutical treatments) and transform them into services. In the future, these services will be able to virtualize multiple levels of care, connect devices,and move to Personalized Medicine (PM). The Health 4.0 Cyber-Physical System (HCPS) contains several types of computers, communications, storage, interfaces, biosensors, and bio-actuators. The HCPS paradigm permits observing processes from the real world, as well as monitoring patients before, during,and after surgical procedures using biosensors. Besides, HCPSs contain bio-actuators that accomplish the intended interventions alongwith other novel strategies to deploy PM. A biosensor detects some critical outer and inner patient conditions and sends these signals to a Decision-Making Unit (DMU). Mobile devices and wearables are present examples of gadgets containing biosensors. Once the DMU receives signals, they can be compared to the patient’s medical history and, depending on the protocols, a set of measures to handle a given situation will follow. The part responsible for the implementation of the automated mitigation actions are the bio-actuators, which can vary from a buzzer to the remote-controlled release of some elements in a capsule inside the patient’s body.Decentralizing health services is a challenge for the creation of health-related applications. Together, CBIR systemscan enable access to information from multimedia and multimodality images, which can aid in patient diagnosis and medical decision-making.Currently, the National Health Service addresses the application of communication tools to patients and medical teams to intensify the transfer of treatments from the hospital to the home, without disruption in outpatient services.HCPS technologies share tools with remote servers, allowing data embedding and BD analysis and permit easy integration of healthcare professionals’expertise with intelligent devices. However, it is undeniable the need for improvements, multidisciplinary discussions, strong laws/protocols, inventories about the impact of novel techniques on patients/caregivers as well as rigorous tests of accuracy until reaching the level of automating any medical care technological initiative.Keywords:Pervasive healthcare, Wireless communications, Cyber-physical Systems, mHealth, Hospital Information System (HIS), Smart pharmaceuticals, Medical databases CITE AS: Estrela, V. V., A. C. B. Monteiro, R. P. França, Y. Iano, A. KHELASSI, and N. Razmjooy. “Health 4.0 As an Application of Industry 4.0 in Healthcare Services and Management”. Medical Technologies Journal, Vol. 2, no. 4, Jan. 2019, pp. 262-76, https://doi.org/10.26415/2572-004X-vol2iss1p262-276 http://ichsmt.org/Journals/ojs/index.php/MTJ/article/view/205.
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Health 4.0: Applications, Management,
Technologies and Review
Type of article: Review
Ana Carolina Borges Monteiro1, Reinaldo Padilha França1, Vania V. Estrela2, Yuzo Iano1,
Abdeldjalil Khelassi3, Navid Razmjooy4
1State University of Campinas (UNICAMP), Brazil, {monteiro@decom.fee.unicamp.br,
reinaldopadilha@msn.com, yuzo@decom.fee.unicamp.br }
2 Universidade Federal Fluminense, Rio de Janeiro, Brazil, vania.estrela.phd@ieee.org
3 University of Tlemcen, Algeria { khelassi.a@gmail.com }
4 Department of Electrical and Control Engineering, Tafresh University, Tafresh 39518 79611, Iran
{ navid.razmjooy@ieee.org }
Abstract
The Industry 4.0 Standard (I4S) employs technologies for automation and data exchange through
cloud computing, Big Data (BD), Internet of Things (IoT), forms of wireless Internet, 5G
technologies, cryptography, the use of semantic database (DB) design, Augmented Reality (AR)
and Content-Based Image Retrieval (CBIR). Its healthcare extension is the so-called Health 4.0.
This study informs about Health 4.0 and its potential to extend, virtualize and enable new
healthcare-related processes (e.g., home care, finitude medicine, and personalized/remotely
triggered pharmaceutical treatments) and transform them into services.
In the future, these services will be able to virtualize multiple levels of care, connect devices, and
move to Personalized Medicine (PM). The Health 4.0 Cyber-Physical System (HCPS) contains
several types of computers, communications, storage, interfaces, biosensors, and bio-actuators.
The HCPS paradigm permits observing processes from the real world, as well as monitoring
patients before, during, and after surgical procedures using biosensors. Besides, HCPSs contain
bio-actuators that accomplish the intended interventions along with other novel strategies to
deploy PM. A biosensor detects some critical outer and inner patient conditions and sends these
signals to a Decision-Making Unit (DMU). Mobile devices and wearables are present examples of
gadgets containing biosensors. Once the DMU receives signals, they can be compared to the
patient’s medical history and, depending on the protocols, a set of measures to handle a given
situation will follow. The part responsible for the implementation of the automated mitigation
actions are the bio-actuators, which can vary from a buzzer to the remote-controlled release of
some elements in a capsule inside the patient’s body.
Decentralizing health services is a challenge for the creation of health-related applications.
Together, CBIR systems can enable access to information from multimedia and multimodality
images, which can aid in patient diagnosis and medical decision-making.
Currently, the National Health Service addresses the application of communication tools to
patients and medical teams to intensify the transfer of treatments from the hospital to the home,
without disruption in outpatient services.
HCPS technologies share tools with remote servers, allowing data embedding and BD analysis and
permit easy integration of healthcare professionals’ expertise with intelligent devices. However, it
is undeniable the need for improvements, multidisciplinary discussions, strong laws/protocols,
inventories about the impact of novel techniques on patients/caregivers as well as rigorous tests of
accuracy until reaching the level of automating any medical care technological initiative.
Keywords: Pervasive healthcare, Wireless communications, Cyber-physical Systems, mHealth,
Hospital Information System (HIS), Smart pharmaceuticals, Medical databases
Corresponding author: Vania V. Estrela, Universidade Federal Fluminense, Rio de Janeiro, Brazil,
vania.estrela.phd@ieee.org
Received: 31 December, 2018, Accepted: 02 January, 2019, E nglish editing: 04 January, 2019,Published: 05 Januar y, 2019.
Screened by iThenticate..©2017-2019 KNOW LEDGE KINGDOM PUBLISHING.
1. Introduction
The Internet of things (IoT) entails sets of gadgets, vehicles, and home equipment
that contain hardware, programming, actuators, and network support, which
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enables to interface and trade data. Hence, these devices can impart and join
forces over the Internet possibly using remote observation and control.
The Internet of Services (IoS) paradigm can connect gadgets intelligently. Makers
need to thoroughly consider their business models to meet their expectations
properly with a long haul income stream. Numerous producers may perceive this
and exploit the chance to enhance their activities. The individualization of large-
scale manufacturing and the IoSs include extra income. The savvy plant should be
adaptable and convey intelligent items. A noteworthy misconception is not a cost
sparing activity. Instead, it is another business model to expand income and
gainfulness.
The (IoT), the IoS and so forth can comply to the Industry 4.0 standard [11] since
it allows for the physical processes’ virtualization and their transformation into
services [4, 5, 45] having in mind for the health domain that things such as
artificial organs, biosensors, smart devices [20], and smart pharmaceuticals are
already available. Hereafter, services will turn around these objects to virtualize
several levels of care, help patients and healthcare professionals to reach
independence, link up devices and technologies, and move towards the
personalized medicine [5, 6].
Figure 1. Zachman framework and the value chain reference model [6, 7, 8, 9, 10]
Smart Industry 4.0 compliant plants are context-aware and assist people with
equipment to execute tasks. The term context-aware means the system can
consider context data, for instance, the QR code, position, and status of an item.
These systems depend on data to conclude their real life and virtual tasks. Real
world evidence, e.g., temperature, position, operation time and condition of an
instrument, in contrast to virtual data, like e-documents, multimedia content, and
simulation results [1, 6, 7, 8, 9, 10]. Clinics and distributed healthcare providing
arrangements such as General Practice (GP) networks, public nurses, pharmacies,
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and so on being similar to factories, to facilitate context-aware people assistance
and the correct use of machines during their duties, which can happen in Hospital
Information Systems (HIS) or practices IT systems. Existing flaws in healthcare
facilities and settings are frequently related to real-time information since it is
limited and that makes workflows difficult to be depicted accurately. Sometimes,
the patient or expert location or her/his current status are not known. The regular
fall-out can be a disruption of the operating schedules with the medical staff
waiting for the patient in the operating room or if patients wait for extended times
in Accident & Emergency (A&E) and outpatient sectors. Smart medical plants
need to incorporate some of the practices from the industrial domains.
Figure 2. The proposed framework for the Health 4.0
2. Industry 4.0 Scalability into the Health Domain
Through the Industry 4.0, a Cyber-Physical System (CPS) can monitor real-world
processes, producing corresponding a virtual rendition of the setting and
implement decision-making in a decentralized fashion. Over the IoT, a CPS
converses and cooperates with humans in real-time. Likewise, the IoS offers both
inner and cross-organizational services for the value chain partakers [1, 2, 3, 4, 5,
6, 7, 10, 33].
Value Chain (VC) Organization (VCO) is paramount to healthcare facilities to
boost their usefulness and productivity in the presence of budget pressures (Figure
1). VCO involves the activities within the organizational boundaries form the VC,
which, in turn, forms parts of the supply chain while linking suppliers with
customers [6, 7, 10].
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Both the flow and the pathways for patients are the traditional VCs’ paradigms
within the healthcare business, which resemble any other business concerning
VCs and can benefit from the Industry 4.0 standard. Healthcare will undoubtedly
be organized modularly as specialization increases, and the global healthcare
model is gradually shifting from a hospital-based professional-oriented to a
distributed patient-centered healthcare model [8]. A distributed patient-centered
healthcare system [34, 35, 36] must have its elements and services available to the
health-deprived subject and the associated caretakers. CPSs still need
popularization in the medicinal domain, but the process has started.
Pharmaceutical corporations are developing smart biosensors and pharmaceuticals
to enable real and virtual worlds communications. Big Data (BD) strategies will
be responsible for individualization and custom-made healthcare. Novel strategies
such as precision medicine relied on real-time connectivity concerning real-world
patients, cloud-based procedures and virtually deployed autonomous systems.
These tactics will combine cross-organizational services depending heavily on
real-time information demanding new health supervision models demand
individual patient budgets offering to patients and informal caretakers more
impact and control of their health and the pertinent resources at their disposal [9].
3. Industry 4.0 Design Principles
According to [1], Industry 4.0 design principles are the following:
• Interoperability;
Virtualization;
Decentralization;
Real-time capability;
Service orientation; and
Modularity.
Interoperability simplifies the contextual information flow on all levels.
Observing the biosensors as part of CPSs and their backend in the virtual domain
smooth interoperability is essential to enable the whole system loop to accomplish
and continuously exchange data. In CPSs, it is also vital to have different services
combined and integrated to significantly improve data readings to guarantee
creation and recording of meaningful data. Thus, interoperability arises as a
fundamental design principle of Health 4.0 solutions [34, 35, 36].
Virtualization must be available, but some issues must be addressed. A CPS can
monitor the physical processes while creating a virtual copy of the reality at a
given time. Smart factories have virtual models that include the condition of all
other CPSs. Almost certainly; these trends are usable for the health domain in
several ways. The observing physical processes are the key to deploy health-
related processes on a daily basis. CPSs will be monitoring patients through
surgical procedures extensively in a standardized fashion everywhere [19].
Nonetheless, the patients’ biosensors during surgery can still benefit from novel
solutions. One key problem is that generally, these islands are equivalent to
closed-loop systems. Hence, they cannot be easily connected to other systems,
e.g., the Picture Archiving and Communication System (PACS). Moreover,
thanks to the human beings’ multifariousness as a system, to copy of the entire
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reality periodically is unfeasible. However, a valid question is undoubtedly in how
far this is sensible and necessary. The monitoring and virtualization of defined
system sections might be sufficient until future technologies allow for more wide-
ranging and easier virtualization. Currently, the challenge in the healthcare
domain is that autonomous virtualization happens anywhere, anyway and
whenever needed. This is particularly interesting to new strategies for the
individualization of therapies (mainly to treat long-lasting, non-communicable
illnesses [12])
Figure 3. Mobile Edge Cloud MEC in the healthcare domain [22]
Healthcare decentralization is generally considered as challenging since it does
give sufficient acclaim and, consequently, it does not appeal to most
underdeveloped nations. Otherwise, progressively more patients will have GP
surgeries, day treatment centers, their households, and over the Internet. Still, as
more smart devices, wearables, and bio-actuators are sold for both fitness and
welfare, these gadgets' accuracy and appropriateness are questionable.
Governance and liability matters are still pendent. The estimated amount of
applications for healthcare, wellness, and beauty on the market is high, which
calls for more rigorous testing of equipment and apps. Concerning accuracy, even
fewer give warranties. Although healthcare is leaning toward a distributed patient-
centered model with patients, specialists and formal and informal caretakers
progressively using smart devices [20], biosensors, bio-actuators, apps, and CPSs,
ever more sophisticated needs are building up about the network and
communications providers. Distributed patient-centered care requires a
continuous and reliable data flow across diverse networks and spheres. The
refined necessities of various domains comprising healthcare have led to
numerous research works [16, 17]. The National Health Service (NHS) addresses
a strategy to apply information communication tools for patients, their caretakers
and medical staff to intensify the number of treatments transfers from hospital to
homecare without disturbing their outpatient services too much [18].
The use of technology like barcode and Radio-Frequency Identification (RFID) in
Industry 4.0 plants can help autonomous decision-making as happens in the NHS
and other countrywide healthcare services [19, 20]. An additional compelling
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aspect is intelligence and processing deployment into networks with distributed
parts. The Mobile Edge Cloud (MEC) computing arrangements are more than a
tendency [22] (Figure 3). It is an effort to support decentralized network
policymaking to diminish latency and augment security. MEC is a popular subject
among network technology providers due to a rising trend towards
decentralization in the healthcare realm, then a robust technology to deploy the
Industry 4.0 design principles. This growth is vital to release efficiency assets in
healthcare and meet future socio-economic requirements [22].
The real-time functionality can be paramount for any organization involving
persons, notwithstanding the domain to safeguard and maintain performance. Part
of the customization rationale is the real-time identification of people’s necessities
in a distributed fashion. Patients should receive wherever possible treatment
outside hospitals with the exact amount of medication to maximize therapeutic
results while lessening secondary effects. This is known as theragnostics [18]
where (ideally) diagnostics, control, relief, and remediation merge and approach
real-time [21] to form a harmonious spatiotemporal entity. This functionality is
crucial to healthcare and it will lead to the implementation of personalized
prescriptions, smart medications, intelligent bio-actuators use, and supply chain
administration.
Service Orientation (SO) in healthcare organizations are shifting to a customer-
SO with reactive groups waiting on the platform. A high-level overview of
customer-centered service aggregation of the IoT, the IoS, the IoP and so forth
combined with customized services [1, 11] also work for healthcare where SO can
be part of the Health 4.0 standard [11, 19]. Likewise, pharmaceutical industries
tend to shift from being only medications manufacturers to become healthcare
service providers. The underlying rationale is to harvest BD from a colossal
variety of smart devices, biosensors and bio-actuators to work with smart devices,
biosensors, and bio-actuators to prevent any harm and serious episodes, lessen
sick days in addition to hospital admission. These policies will expand the life
quality and decrease associated prices and dependencies. This means that a
health-related company will sell disease administration procedures as services
from a business model viewpoint. In contrast, healthcare suppliers may soon only
receive products that exceed the plain delivery of treatment or medications.
Patients’ healthcare records deliverance via a distinct interface may become an
admission prerequisite to access some healthcare. Patients can sanction the use of
their healthcare data as a service and sell this information to healthcare
establishments to speed up trials (in the case of ill-doing) or the creation of new
lines of disease attack. All these currentspeculations and the forthcoming 5G
technology will enhance the healthcare domain SO. Eventually, both the network
slice expertise, the MEC technologies, and possibly novel developments will help
to set up service aggregation through different realms and networks [22].
Modularity in healthcare is important because a modular system can adjust to
changing settings and requirements while replacing or expanding subsystems.
Consequently, modular systems can be effortlessly adjusted to handle seasonal
fluctuations or changes in products/systems characteristics. [22, 38, 39]. Modular
software components must engender new functionalities by just recombining
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different active groups. Enforcing the rules to reflect norms and standards
alongside the system's software modules is an effective approach to program
faster and instituting new functionalities from predefined software building blocks
[23]. Users may consult and evaluate a collection of services and characteristics to
choose products that fulfill their needs and wants [24, 37].
From the healthcare domain viewpoint, the security dimension is worth
commenting [1] since it is a critical infrastructure for any nation that calls for
protection of all the interrelated functionalities and the confidentiality of the
personal data. These delicate matters differ somehow from a smart factory where
security fissures may cause monetary losses or material damage without huge
liability for personal data or loss of lives. Issues such as safety, robustness,
confidentiality, and resilience while sparing all participants from incalculable and
random risk are hard to design principles from a Health 4.0 angle. Trust is both a
healthcare pivotal principle and a legal obligation addressed by most national
legislation. The Health 4.0 domain needs to prioritize safety, robustness, and
resilience as a general because in the Industry 4.0 these topics are basic
requirements but do not tackle issues so delicate concerning human matters as
health is stated by the WHO. A possibility is to extend the Industry 4.0 design
criteria to the health domain [1].
4. Health 4.0
Health 4.0 is a tactical deployment, and managerial model for healthcare inspired
by the Industry 4.0. Health 4.0 has to allow gradual virtualization to support the
healthcare personalization close to real-time for patients, workers, and both
formal and informal caretakers. This healthcare personalization calls for the
substantial usage of CPSs, cloud computing, the extended specialized IoTs aka
Internet of Everything (IoE) including appliances, services, people, and surfacing
5G communication networks. With the help of the CPS paradigm, software fit for
distributed systems and BD tools, algorithms, and objects will be virtualized
employing a spatial-temporal matrix. The virtualization permits the inspections of
small space-time windows of the real world in real-time and, thus, allows for
theragnostics [12, 18] in personalized and precise medicine.
4.1 Improving Health Services through CPS
Adherence
The improvement of medical analyses must comply with the Industry 4.0 protocol
with long-lasting behavioral modifications. These objectives must incorporate
technologies like 5G, IoT, Narrow Band IoT (NB-IOT [12, 13]), network slices
[13], cloud computing, Big Data (BD), and cryptography/security into real-time
CPSs.
Some technology alternatives involve the use of embedded biosensors and bio-
actuators. At first, smartphones were used as back-end devices to save data and
deliver processing intelligence by creating connections to healthcare
professionals. Smartphones can function as gateways to share information with
remote servers hence enabling incorporation of data and BD analysis. Although
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smartphones can work like computers and potential gateways, there are some
questions concerning reliability, appropriateness, and practicality.
Characteristically, patients require different sorts of medications and medical
equipment. Many patients stock several items in different places can be reached
readily in case of need. This means that many medical types of equipment would
be connected to the smart device permanently, which demands battery life and
routers. Another challenge is the conflict regarding the reliability between mobile
and medical devices as a whole. Although mobile devices normally function on a
best effort basis, medical devices can be mission critical. The concepts of best
effort and mission-critical task comprise the Quality of Service (QoS) but they
mean different things. The best effort concept decides if sensitive patients’ data
will be sent, while the term mission critical guarantees the reliability of
equipment, organization or process. Evidently, to guarantee a high Quality of
Care (QoC) along with superior QoS and Quality of Experience (QoE) of medical
devices, they should not work on a best effort basis since this can jeopardize the
key objectives of a therapy, i.e., maximizing adherence [21] while minimizing the
incidence of severe symptoms, attacks, hospitalization, and even death. Other
significant aspects comprise energy efficiency and data protection and
confidentiality at all levels. Exhausting mobile devices and wearables as gateways
can improve energy efficiency but using smart devices while accessing the radio
network is demeaning to energy efficient. Security communication links’ and
smart devices’ biosensors’ and actuators’ signals need to be safe while complying
with the standards and legal difficulties from the healthcare field [19].
Unusual technologies can expressively improve the current communication links
for smart pharmaceuticals, intelligent treatments of varied forms and smart
healthcare deployment strategies. NBIOT requires a Low Power Wide Area
(LPWA) to allow robustness to power shortages with all sorts of electromagnetic
frequencies including Radio Access Technology (RAT). Smart power meters have
employed similar knowhow to gauge households’ consumptions more accurately.
Still, this does not exclude smart devices, biosensors, and bio-actuators of being
part of individualized therapy sometimes. Patients can manage their data and get
therapy recommendations with their smart devices through video downloads for
instance. They can also permit and follow up their information usage by
healthcare professionals, caretakers, and researchers.
NBIOT modules are an exciting and feasible solution to enhance connectivity by
exploiting different segments of the electromagnetic spectrum in contrast to
smartphones. In general, NBIOT employs considerably lower frequencies than
smartphones. Low-frequency waves have superior penetration and reach
properties owing to their physical properties. However, the data bulk and the
bandwidth are limited by the energy efficiency of the smart gadgets. The issue is
if a medication cost increase can be justified through the value aggregated when
using Industry 4.0 characteristics, such as CPS capability, modularity (to allow for
several biosensors, bio-actuators and medications pertaining to a person to
interact simultaneously), SO and interoperability (which means that services and
CPSs may be combined to attain better disease control). Fundamental
requirements for Health 4.0 are:
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• Predictable QoS;
• Network agnostic and interoperable technology;
• Safe, robust, privacy-protected, and resilient technology;
• Complete connectivity and compatibility; and
Worldwide product/service interoperability as well as network capabilities for a
global service organization.
4.2 Medical Internet of Things (mIoT)
Lack of knowledge about a health problem and the corresponding proper
management can aggravate conditions and result in high mortality. The successful
application of mIoT in diseases’ management and health education are essential
issues. With mIoT and 5G, all kinds of multimedia material related to disease
education can be sent to the patients’ mobile terminals, augmenting their
knowledge about their conditions while integrating pharmacological and non-
pharmaceutical treatments. In addition, mIoT facilitates the assessment and
monitoring of illnesses. For example, patients can control their tests and
questionnaires using cell phones habitually, so that doctors can observe their
patients’ states regularly. Alternatively, health specialists, decision-makers, and
service providers can apply the mIoT to assess conditions dynamically and how
they interact with environmental or behavioral aspects [13, 14, 15, 16, 17, 28].
4.3 Databases
The advent of Content-Based Image Retrieval (CBIR), Content-Based Video
Retrieval (CBVR) and Content-Based Multimedia Retrieval (CBMR) systems
have countless applications in medical settings. This manuscript will refer to them
as Content-Based Retrieval (CBR) systems.
Figure 4. CBIR and CBVR in health care [24, 25]
A CBIR accesses information from still images and this way of diagnosing
patients allows for an intuitive case representation for physicians and healthcare
staff. The corresponding information can be structured by cases, which leads to a
suitable way the semantic gap can be solved through medical semantics and
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semiotics knowledge and structured databases. Moreover, the multimedia data
comprehension occurs incrementally in a CBR system. Besides these CBR
benefits, there exist Translational Incremental Similarity-Based Reasoning
(TISBR) structures [23] where the reasoning relies on the combined
characteristics of different types of CBR systems containing information obtained
with several modalities. A TISBR can provide incremental medical knowledge
learning, collections of structured CBR cases, the multimedia usage of similar
cases, and more information about different concepts of similarity. These
objectives can be accomplished by means of the indexing, the use of cases
retrieval, and the search refinement strategies [24, 25].
Better databases will also allow healthcare professionals to build more
intelligence representations of the data with more information per pixel or voxel
as well as high-resolution models with the help of deep learning [28, 29, 30, 31,
32].
4.4 5G-Driven Personalized Care
The health treatments use different tools and procedures that need to comply with
the commercially available smart devices, biosensors, actuators and wearables
connected to medical CPSs. Furthermore, schemes adequate to custom-made
medicine need to be devised. The latest generation of smart gadgets, biosensor,
and bio-actuators technologies supports some surveillance of important treatment
Key Performance Indicators (KPIs) at the healthcare points. KPIs like adherence
[21], physiological parameters, and 5G timing will afford multi-frequency
connectivity with multi-modal capability comprising NBIoT and mobile
communication to enable the real and the virtual realms to exchange information.
This will facilitate theragnostics procedures and the smooth combination of
several types of therapies [12, 26, 27, 33].
Theranostics is a treatment approach that associates both (i) diagnostic tests to
spot patients most predisposed and in need to be helped or affected by a new
medication, and (ii) a targeted therapy built on the test results. Genomics,
proteomics, bioinformatics, and functional genomics are molecular biology
technologies indispensable for the molecular theragnostics evolution. These
technologies engender the genetic and protein data essential for the expansion of
diagnostic examinations. Theragnostics comprises an extensive range of subjects,
including pharmacogenomics, personalized remedies, and molecular imaging to
produce effective novel targeted therapies with acceptable benefit/risk to patients
and an improved molecular understanding to optimize medicine selection.
Moreover, theragnostics can (i) monitor the treatment response, (ii) augment drug
efficacy and security, and (iii) abolish superfluous or wrong patients’ therapies.
The final result is a significant cost reduction on behalf of the healthcare system.
Nevertheless, introducing theragnostics tests into routine healthcare entails both a
cost-effectiveness analysis and the readiness of proper and accessible testing
systems [18, 20]. The usage of smart devices, biosensors, and bio-actuators will:
• Decrease the occurrence of serious incidents;
• Improve the efficiency of therapies;
• Expand the QoE for patients and professionals;
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• Shrink the number of patient admissions, sick days absences, and outpatient
inspections; and
• Advance e-documentation and personal risk analysis.
5. Discussion
Numerous studies have pointed out that telemedicine will improve healthcare
quality for patients. Several telemedicine applications show the potential CPSs for
healthcare access improving quality, and efficiency in healthcare and
telemedicine.
However, a key factor is how to design such systems using the CPS paradigm.
Nevertheless, many organizations, healthcare centers, governmental agencies,
private medical institutions, and medical professionals still need to discuss and
assimilate the technological challenges involved in telemedicine systems.
Furthermore, it is paramount to consider a privacy policy regarding patients' data
due to the delicate nature of medical/healthcare information.
Future developments will analyze solutions from other places and how they can
be translated into Healthcare CPSs [2, 3, 4, 35]. Moreover, provisions for
intelligent information retrieval and database handling must be thought [24, 25].
The most reasonable line of attack is to extend the Industry 4.0 standard to the
healthcare domain (the so-called Health 4.0). This paradigm shift will permit
healthcare CPSs to be compliant with other automated frameworks.
6. Conclusions
There is profuse evidence that Health 4.0 should borrow concepts from the
Industry 4.0 standard. Due to the very definition of healthcare infrastructures as
critical regarding security, robustness, and resilience, additional design principles
for Health 4.0 have to be recognized as obligatory. Mobile devices can become
future gateways for intelligent healthcare despite their present-day restricted
communication routing ability, limited battery life, and best effort paradigm.
Mobile gadgets must be compliant with smart healthcare via smart devices and
general custom-made medicine methodologies (mission-critical QoS, multi-
tenancy, and high QoE). However, smart devices and wearables will impact the
transmission and reception of multimedia files and reports. Smart devices,
biosensors, and bio-actuators will benefit from 5G technologies including NB
IoT will be available soon, which will prompt innovative business models. The
healthcare industry might swing from the manufacturing to the service business
by embracing new duties.
7. Conflict of interest statement
We certify that there is no conflict of interest with any financial organization inthe
subject matter or materials discussed in this manuscript.
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273
8. Authors’ biography
A. C. B. Monteiro
M.Sc. in Electrical Engineering from the State University of Campinas in
telecommunications and signal processing. Ph.D. Candidate at Department of
Communication (DECOM) da Universidade Estadual de Campinas (UNICAMP).
Reinaldo P. França
M.Sc. in Electrical Engineering from State University of Campinas in biomedical
engineering and image processing. PhD Candidate at Departament of Communication
(DECOM) da Universidade Estadual de Campinas (UNICAMP).
Vania V. Estrela
B.Sc. degree from Federal University of Rio de Janeiro (UFRJ) in Electrical and
Computer Engineering (ECE); M.Sc. in ECE from the Technological Institute of
Aeronautics (ITA) and Northwestern University, USA; and Ph.D. in ECE from
the Illinois Institute of Technology (IIT), Chicago, IL, USA. Taught at: DeVry
University; State University of Northern Rio de Janeiro (UENF), Rio de Janeiro
(RJ), Brazil; and for the West Zone State University (UEZO), RJ. Research
interests include signal/image/video processing, inverse problems, computational
& mathematical modeling, stochastic models, multimedia, electronic
instrumentation, machine learning and remote sensing. Reviewer for: IMAVIS
(Elsevier); Pattern Recognition (Elsevier); COMPELECENG (Elsevier);
Measurement (Elsevier); IET Image Processing; EURASIP Journal on Advances
in Signal Processing (JASP) (Springer); IEEE Journal of Biomedical and Health
Informatics (JBHI); Int’l J. of Electrical and Comp. Engineering (IJECE); Int’l
Journal of Ambient Computing and Intelligence (IJACI); Journal of Microwaves,
Optoelectronics and Electromagnetic Applications (JMOE); and SET Int'l J.
Broadcast Eng. (SET-IJBE). Engaged in topics such as technology transfer,
STEM education, environmental issues and digital inclusion. Member of IEEE,
and ACM. Editor of IJACI, EURASIP JASP, and SET-IJBE.
Yuzo Iano
B.Sc. by the State University of Campinas/SP/Brazil-Unicamp in Electrical
Engineering (1972), M.Sc. in Electrical Engineering from State University of
Campinas (1974) and doctorate at Electrical Engineering from the same university
(1986). He is currently full professor at Unicamp. Has experience in Electrical
Engineering, focusing on Telecommunications, Electronics and Information
Technology. He is working in the following subjects: digital transmission and
processing of images/audio/video/data, HDTV, digital television, networks
4G/5G, middleware, transmission, canalization, broadcasting of television signals,
pattern recognition, digital coding of signals, data transmission and storage, and
smart/digital cities.
Dr Abdeldjalil Khelassi: is an Associate Professor at Tlemcen University,
Algeria. He obtained his Doctor in Science (2013), Magister (2008) and Engineer
(2004) in Computer Sciences from the Department of Computer Science at
Tlemcen University. His research interest includes cognitive systems, knowledge-
based systems, case-based reasoning, distributed reasoning, fuzzy sets theory and
health science. He is the editor manager of Medical Technologies Journal and the
co-editor in chief of Electronic Physician Journal.
Medical Technologies Journal, Volume: 2, Issue: 4, January-March 2018, Pages: 262-276. Doi :
https://doi.org/10.26415/2572-004X-vol2iss1p262-276
274
Navid Razmjooy
B.Sc. by the Ardabil branch of IAU University/Iran (2007), M.Sc. from the
Isfahan branch of IAU University/Iran with honor in Mechatronics Engineering
(2011), Ph.D. in Control Engineering (Electrical Engineering) from by the
Tafresh University/Iran (2018). Born born in 1988. Research opportunity at the
Amirkabir University of Technology (2017-2018). He is working in the following
subjects: Control, interval analysis, Optimization, Image Processing, Machine
Vision, Soft Computing, Data Mining, Evolutionary Algorithms, and System
Control. He is a senior member of IEEE/USA and YRC in the IAU/Iran. He
published more than 75 papers and 3 books in English and Farsi in peer-reviewed
journals and conferences and is now a reviewer in the national and international
journals and conferences.
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Social participation exists due to empathy for oneself. A single unit is far more vulnerable to death and the vagaries of nature than the collective. Yet, living with others is a dangerous affair, best understood when going through a history of power struggles and extensive bloodshed. To cohabit successfully, we need socially applied ethics that counter the egocentric design of human nature. In Europe, ethics has been brought to us by divine intervention. Tensions within evolving societies, born of intolerable injustice in the temporal world, necessitated the codification of God's ethical word into law upheld by the living. The secularization of the state progressed to create an inclusive society, reflective of difference. Life spans were prolonged, the quality of life-bettered. A single glitch occurred in the system. Social ethics outrunned state's ability to adjust financially to the changes. Today, complaints are being voiced as to a strain in empathy provision towards society's main working unit-the individual. Sadly, the brunt of the criticism has been absorbed by Europe's healthcare system. In places, the cries of patients' indignation have been matched by shocking, self-promoting profiteering.
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The growing use of digital image processing techniques focused on health is explicit, helping in the solution and improvements in diagnosis, as well as the possibility of creating new diagnostic methods. The blood count is the most required laboratory medical examination, as it is the first examination made to analyze the general clinical picture of any patient, due to its ability to detect diseases, but its cost can be considered inaccessible to populations of less favored countries. In short, a metaheuristic is a heuristic method for generally solving optimization problems, usually in the area of combinatorial optimization, which is usually applied to problems for which no efficient algorithm is known. Digital Image Processing allows the analysis of an image in the various regions, as well as extract quantitative information from the image; perform measurements impossible to obtain manually; enable the integration of various types of data. Metaheuristic techniques have come to be great tools for image segmentation for digitally segmenting containing red blood cells, leukocytes, and platelets under detection and counting optics. Metaheuristics will benefit to computational blood image analysis but still face challenges as cyber-physical systems evolve, and more efficient big data methodologies arrive.
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Official Open Access Article Available at http://www.mdpi.com/2227-7080/6/2/42 An Emergency Response (ER) Cyber-Physical System (CPS) to avoid landslides and survey areas located on or near slopes is introduced that handles two problems: electronic waste disposal, and environmental disasters. Uncomplicated detection circuits using salvaged components can pinpoint landslides in impoverished regions. CPSs simplify hazard prediction and mitigation in disaster supervision. Nonetheless, few green practices and efforts have been accomplished in this regard. Recent technical advances help landslides studies and the evaluation of suitable risk alleviation measures. This work addresses in situ meters, and cameras to observe ground movements more accurately. The ER-CPS identifies and can help mitigate landslides using techniques based on motion detection that can productively predict and monitor the zone conditions to classify it, and the landslide-related data can be transmitted to inspecting stations to lessen the erosion/sedimentation likelihood while increasing security.
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Introduction: Lung cancer is the most wide spread from of cancer, with the highest mortality rate worldwide. In this study, a computer-aided detection (CAD) system was developed for lung nodule detection, segmentation and recognition using CT images. So, we use a highly accurate supervised that uses lung images with the aim of assisting physicians in early detection of lung cancer. Methods: First, we segmented the lung area by masking techniques to isolated nodules and determined region of interest. Then, 24 features were extracted from images that included morphological, statistical and histogram. Important features were derived from the images for their posterior analysis with the aid of a harmony search algorithm and fuzzy systems. Results: In order to evaluate the performance of the proposed method, we used the LIDC database. the number of images included a database of 97 images whom 47 were diagnosed with lung cancer. Results of the base method show a sensitivity of 93%. Conclusion: The harmony search algorithm is optimized using fuzzy system for classification. The CAD system provides 93.1% accuracy.
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Introduction: Access to Health Information is very essential for promoting health literacy, self-care, shared decision making, medication adherence. The Internet is one of the main resources of health information. Many studies showed the main gateway for seeking health information was search engines. But finding qualified health information is a challenge. Therefore, this study aimed to introduce top health Searches engines and review their features. Methods: According to the literature review, 10 health Searches engines were selected. Common features of each search engine, such as the ability to create profiles, the type of health information provided, target users, health information sources contained in the search engine, and the unique feature and other features, were reviewed and the comparison table was provided. A common search scenario was also tested on all search engines and the result of the data retrieval was reported by the search engine. Results: health Searches engine like that PubMed، Med scape، McGraw-Hill Medical، iMediSearch، medicin.net, Hardin.Md، Health line ، EMedicine، Merck and Ovid were chosen and reported. Conclusion: The results of this study can help users such as patient to choose a valid health searches engine and also help them to find health searches engine appropriate for their health problem and know what features in health website is important.
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Major interest is currently given to the integration of clusters of virtualization servers, also referred to as 'cloudlets' or 'edge clouds', into the access network to allow higher performance and reliability in the access to mobile edge computing services. We tackle the edge cloud network design problem for mobile access networks. The model is such that the virtual machines (VMs) are associated with mobile users and are allocated to cloudlets. Designing an edge cloud network implies first determining where to install cloudlet facilities among the available sites, then assigning sets of access points, such as base stations to cloudlets, while supporting VM orchestration and considering partial user mobility information, as well as the satisfaction of service-level agreements. We present link-path formulations supported by heuristics to compute solutions in reasonable time. We qualify the advantage in considering mobility for both users and VMs as up to 20% less users not satisfied in their SLA with a little increase of opened facilities. We compare two VM mobility modes, bulk and live migration, as a function of mobile cloud service requirements, determining that a high preference should be given to live migration, while bulk migrations seem to be a feasible alternative on delay-stringent tiny-disk services, such as augmented reality support, and only with further relaxation on network constraints.
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The application of data mining (DM) in healthcare is increasing. Healthcare organizations generate and collect large voluminous and heterogeneous information daily and DM helps to uncover some interesting patterns, which leads to the manual tasks elimination, easy data extraction directly from records, to save lives, to reduce the cost of medical services and to enable early detection of diseases. These patterns can help healthcare specialists to make forecasts, put diagnoses, and set treatments for patients in health facilities. This work overviews DM methods and main issues. Three case studies illustrate DM in healthcare applications: (i) In-Vitro Fertilization; (ii) Content-Based Image Retrieval (CBIR); and (iii) Organ transplantation. ISSN 2320-8481
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Content-Based Image Retrieval (CBIR) locates, retrieves and displays images alike to one given as a query, using a set of features. It demands accessible data in medical archives and from medical equipment, to infer meaning after some processing. A problem similar in some sense to the target image can aid clinicians. CBIR complements text-based retrieval and improves evidence-based diagnosis, administration, teaching, and research in healthcare. It facilitates visual/automatic diagnosis and decision-making in real-time remote consultation/screening, store-and-forward tests, home care assistance and overall patient surveillance. Metrics help comparing visual data and improve diagnostic. Specially designed architectures can benefit from the application scenario. CBIR use calls for file storage standardization, querying procedures, efficient image transmission, realistic databases, global availability, access simplicity, and Internet-based structures. This chapter recommends important and complex aspects required to handle visual content in healthcare.